Spark Programming in Python for Beginners with Apache Spark 3 - Configuring Spark Session

The video tutorial explains four methods to configure Apache Spark: environment variables, sparkdefaults.com file, spark-submit command line, and application code. It discusses the precedence of these methods, with application code having the highest precedence. The tutorial advises using command line and SparkConf for configuration, leaving environment variables and sparkdefaults.com for cluster admins. It also covers best practices for deployment and runtime behavior configurations, and suggests avoiding hardcoding by using external config files.

Which method is primarily used by cluster administrators to set default configurations for all Spark applications?

• Coding configurations in the application

• Environment variables

• spark-submit command line options

• spark-defaults.conf file

Which configuration method allows you to set properties like 'spark.app.name' directly in the code?

• spark-defaults.conf file

• Coding configurations in the application

• Environment variables

• spark-submit command line options

What is the order of precedence for Spark configuration methods?

• Environment variables, spark-defaults.conf, command line options, application code

• Application code, command line options, spark-defaults.conf, environment variables

• Command line options, application code, environment variables, spark-defaults.conf

• spark-defaults.conf, environment variables, command line options, application code

When configuring deployment-related properties like 'spark.driver.memory', which method is recommended?

• Coding configurations in the application

• spark-defaults.conf file

• Environment variables

• spark-submit command line options

What is the recommended method for setting runtime behavior configurations like 'spark.task.maxFailures'?

• SparkConf in the application

• spark-submit command line options

• spark-defaults.conf file

• Environment variables

What is a suggested approach to avoid hardcoding Spark configurations in the application code?

• Use environment variables

• Utilize a separate configuration file

• Rely on spark-defaults.conf

• Set configurations directly in the application

Why is it problematic to hardcode the 'master' configuration in Spark applications?

• It limits the application to a single deployment environment

• It increases the application's memory usage

• It makes the application run slower

• It causes compatibility issues with different Spark versions